A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome.
A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome.
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DOI：
10.1186/s12866-017-1101-8
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发表时间：
2017-09-13
期刊：
影响因子：
4.2
通讯作者：
Azcarate-Peril MA
中科院分区：
文献类型：
--
作者：
Allali I;Arnold JW;Roach J;Cadenas MB;Butz N;Hassan HM;Koci M;Ballou A;Mendoza M;Ali R;Azcarate-Peril MA
下一代测序（NGS）技术在通量、读取长度和准确性方面的进步，通过显著改善16S rRNA扩增子测序，对微生物组研究产生了重大影响。随着测序平台和新的数据分析管道的快速改进，评估它们在特定应用中的能力至关重要。本研究的目的是评估使用不同的测序平台和生物信息学管道是否可以得出关于微生物组组成的相同项目特异性生物学结论。采用Illumina MiSeq、Ion Torrent PGM和Roche 454 GS FLX Titanium平台，采用标准和改进的文库制备方案，对鸡盲肠微生物组进行16S rRNA扩增子测序分析。我们标记了我们分析中包含的生物信息学管道QIIME1和QIIME2 (de novo OTU选择[不要与QIIME2版本混淆，通常称为QIIME2])， QIIME3和QIIME4（开放参考OTU选择），UPARSE1和UPARSE2（每对仅在使用嵌合体消除方法时不同）和DADA2（仅适用于Illumina数据）。GS FLX+的reads最长，质量分数最高，而MiSeq经过质量过滤后的reads数量最多。在GS FLX+的150-199碱基和MiSeq的90-99碱基处观察到质量分数的下降。pgm生成的数据得分稳定。各平台间总体微生物组组成具有可比性；然而，特定类群的平均相对丰度因测序平台、文库制备方法和生物信息学分析而异。其中，采用全新OTU采摘的QIIME获得的独特物种数量最多，且与QIIME相比，UPARSE和DADA2降低了α多样性。尽管多样性和丰度存在差异，但本研究中比较的三个平台能够通过处理来区分样品，从而得出类似的生物学结论。我们的研究结果表明，尽管覆盖深度和系统发育多样性存在差异，但所有工作流程都显示出对微生物多样性的类似处理效果。为了提高可重复性和可靠性，并保持相似研究之间的一致性，在选择微生物组研究的NGS平台和分析工具时，重要的是要考虑对数据质量和分类群相对丰度的影响。本文的在线版本（10.1186/s12866-017-1101-8）包含补充材料，可供授权用户使用。
Advancements in Next Generation Sequencing (NGS) technologies regarding throughput, read length and accuracy had a major impact on microbiome research by significantly improving 16S rRNA amplicon sequencing. As rapid improvements in sequencing platforms and new data analysis pipelines are introduced, it is essential to evaluate their capabilities in specific applications. The aim of this study was to assess whether the same project-specific biological conclusions regarding microbiome composition could be reached using different sequencing platforms and bioinformatics pipelines. Chicken cecum microbiome was analyzed by 16S rRNA amplicon sequencing using Illumina MiSeq, Ion Torrent PGM, and Roche 454 GS FLX Titanium platforms, with standard and modified protocols for library preparation. We labeled the bioinformatics pipelines included in our analysis QIIME1 and QIIME2 (de novo OTU picking [not to be confused with QIIME version 2 commonly referred to as QIIME2]), QIIME3 and QIIME4 (open reference OTU picking), UPARSE1 and UPARSE2 (each pair differs only in the use of chimera depletion methods), and DADA2 (for Illumina data only). GS FLX+ yielded the longest reads and highest quality scores, while MiSeq generated the largest number of reads after quality filtering. Declines in quality scores were observed starting at bases 150–199 for GS FLX+ and bases 90–99 for MiSeq. Scores were stable for PGM-generated data. Overall microbiome compositional profiles were comparable between platforms; however, average relative abundance of specific taxa varied depending on sequencing platform, library preparation method, and bioinformatics analysis. Specifically, QIIME with de novo OTU picking yielded the highest number of unique species and alpha diversity was reduced with UPARSE and DADA2 compared to QIIME. The three platforms compared in this study were capable of discriminating samples by treatment, despite differences in diversity and abundance, leading to similar biological conclusions. Our results demonstrate that while there were differences in depth of coverage and phylogenetic diversity, all workflows revealed comparable treatment effects on microbial diversity. To increase reproducibility and reliability and to retain consistency between similar studies, it is important to consider the impact on data quality and relative abundance of taxa when selecting NGS platforms and analysis tools for microbiome studies. The online version of this article (10.1186/s12866-017-1101-8) contains supplementary material, which is available to authorized users.
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影响因子：
12.3
作者：
Bonder MJ;Tigchelaar EF;Cai X;Trynka G;Cenit MC;Hrdlickova B;Zhong H;Vatanen T;Gevers D;Wijmenga C;Wang Y;Zhernakova A
通讯作者：
Zhernakova A
影响因子：
48
作者：
Callahan BJ;McMurdie PJ;Rosen MJ;Han AW;Johnson AJ;Holmes SP
通讯作者：
Holmes SP
影响因子：
5.7
作者：
Drumo R;Pesciaroli M;Ruggeri J;Tarantino M;Chirullo B;Pistoia C;Petrucci P;Martinelli N;Moscati L;Manuali E;Pavone S;Picciolini M;Ammendola S;Gabai G;Battistoni A;Pezzotti G;Alborali GL;Napolioni V;Pasquali P;Magistrali CF
通讯作者：
Magistrali CF
影响因子：
3.7
作者：
Devine AA;Gonzalez A;Speck KE;Knight R;Helmrath M;Lund PK;Azcarate-Peril MA
通讯作者：
Azcarate-Peril MA
DOI：
10.1073/pnas.0807920105
发表时间：
2008-11-18
影响因子：
11.1
作者：
Fierer, Noah;Hamady, Micah;Knight, Rob
通讯作者：
Knight, Rob